Business Intelligence (BI) generally refers to software tools used to improve business enterprise decision-making. These tools are commonly applied to financial, human resource, marketing, sales, customer and supplier analyses. More specifically, these tools can include: reporting and analysis tools to present information, content delivery infrastructure systems for delivery and management of reports and analytics, data warehousing systems for cleansing and consolidating information from disparate sources, and data management systems, such as relational databases or On Line Analytic Processing (OLAP) systems used to collect, store, and manage raw data.
A subset of business intelligence tools are Enterprise Information Management (EIM) tools. EIM tools include functions for maintaining and managing the quality of data. EIM tasks include data integration, data quality/cleansing (i.e., defect detection and correction), and metadata management. Other EIM tasks include data profiling, matching and enrichment. EIM tools are useful for organizations to asses the quality of their data and improve the quality thereof. Traditionally, a large part of EIM has been cleansing of customer data (e.g., names and addresses). The EIM tools can be used to profile the data to asses its quality. EIM can be used for product data and financial data. There are a number of EIM tools for the various EIM tasks. Such tools are available from Business Objects, San Jose, Calif.
The EIM task of data quality/cleaning includes the acts of defect detection and reporting on data quality. Data quality is measured in many ways including accuracy, currency, completeness, and consistency. The reports of data quality can be qualitative and quantitative. There are EIM tools with graphical interfaces and dashboard reports. These dashboard reports provide a snapshot of data quality task results showing a graphical summary of an analysis of the data.
There are known techniques for graphically portraying quantitative information. The techniques are used in the fields of statistical graphics, data visualization, and the like. Venn diagrams and graphs can be used to represent sets. Sets and intersections of sets have a logical mapping to Venn diagrams. Sets and their associations may be logically mapped to a graph and thereby facilitate data selection. These visualizations can be included in EIM tools, BI tools, report documents or other documents.
Existing Venn diagrams and graphs have limitations. One limitation of a Venn diagram is that showing the outliers to a set is difficult. There is no place in the visualization to logically map the outliers. Another limitation is that for large numbers of sets, association of two sets (i.e., their overlap) is difficult to depict.
In view of the foregoing, it would be highly desirable to provide improved techniques for the visualization of sets. It would also be desirable to enhance existing BI tools, including EIM tools, to facilitate improved reporting on data quality.